Tax authorities face growing volumes of filings and payments and must manage procedural non-compliance (e.g., late filing, late payment and the accumulation of tax arrears) with limited administrative capacity. Many existing machine learning (ML) and artificial intelligence (AI) applications in tax administration rely on binary outcomes, which limits severity-based prioritisation and the targeting of low-cost interventions. This study develops a multiclass prediction model for administrative tax compliance severity using Slovakia's public tax reliability index, which classifies companies into three categories based on regulator-defined administrative criteria. Using only financial statement ratios and governance indicators, we evaluate nine classifiers and five resampling techniques for class imbalance. Gradient boosting models (XGBoost and CatBoost) perform best, reaching an OvR AUC-ROC above 96% for 1-year forecasts, with modest declines for 2- and 3-year horizons. SHAP explanations indicate that smaller boards and indicators consistent with liquidity constraints and tax-payment pressure are associated with higher-severity administrative classes. The proposed workflow offers a transferable framework for multiclass, long-horizon compliance risk prediction and can support proactive case management (e.g., targeted reminders and payment facilitation, including payment plans, and debt prioritisation) in advance of the regulator's semi-annual updates; it may also provide researchers with a potential early-warning label of administrative compliance frictions that could be examined in relation to financial distress.
Pictures are a powerful medium to communicate complex and emotive messages. In particular, the human face expresses corporate culture including diversity and equal opportunity. However, despite the recent visual turn in accounting and finance, quantitative research on diversity in photos is scant because automated solutions for identifying and classifying human faces were not readily available. This paper seeks to bridge this gap by tailoring automated large-sample facial analysis from the recent computing literature into the accounting literature. Our automated model identifies and classifies faces with sufficient accuracy and precision to draw reliable inferences, and this model is made available for future research. We use the resulting quantitative dataset to analyse intermodal discourse in the annual report, asking the question: Do cover photos augment textual diversity disclosure, or are they PR window-dressing? Results suggest that the decision to publish faces on the annual report cover is associated with an integrated reporting strategy and high-quality diversity disclosure, consistent with pictures augmenting textual disclosures. Gender and ethnic diversity of faces in cover photos tell a different story, tending towards PR window-dressing. Methods and findings from this paper may be of interest to researchers, government and policy makers involved in diversity research and regulation.
Genetic programming (GP) is used to obtain multiperiod bankruptcy prediction models, as well as to perform a prior feature selection process for these models. Given the controversy in the field of bankruptcy prediction about the need to include (or not) variables from the economic environment as input information for the prediction models, an analysis is carried out to check whether the impact that the economic environment undoubtedly has on the firms can be captured using only the financial variables of the firm as explanatory variables. To this end, the analysis includes a study of the correlation between the estimates of the prediction models and certain economic indicators. The results confirm the possibility of capturing the evolution of the economic environment using only financial information as input, as strong correlations are shown between the predictions of the models and important economic indicators over a very long postlearning period (2008-2020) and varied in terms of the economic environment (crisis, recovery, COVID, etc.).
Crypto assets have experienced significant growth in recent years, attracting substantial investments from institutional entities and individual investors alike. This surge in popularity necessitates sophisticated strategies to optimize returns. Concurrently, advancements in machine learning have revolutionized the forecasting of crypto asset returns, facilitating algorithmic trading. Leveraging robust algorithms, this approach enables comprehensive market exploration and capitalizes on escalating computational capabilities. This manuscript presents a comparative analysis of neural networks, genetic algorithms, and fuzzy logic, framed within the ordered weighted average (OWA) operator paradigm. These methods are integrated with deep learning and quantum computing principles to predict price movements in crypto assets and other financial indices. Our findings indicate that the quantum genetic algorithm excels in accurately forecasting asset price trends, whereas the quantum fuzzy approach exhibits comparatively lower precision in predicting cryptocurrency price fluctuations. The empirical analysis employs high-frequency data sampled at 10-, 30-, and 60-min intervals from October 2021 to February 2023. The dataset encompasses 11 cryptocurrencies (e.g., Bitcoin and Ethereum), 10 fan tokens, 10 NFTs, and nine reference financial indices (including Gold, WTI Oil, S&P 500, and Euro Stoxx 60). The implications of this research extend to the development of advanced algorithmic trading strategies, offering valuable tools for market participants and stakeholders in the financial sector. The methodologies discussed herein provide versatile and quantitative frameworks for analyzing diverse financial markets, highlighting their potential to enhance decision-making and improve investment outcomes.
The rise of cryptocurrencies has generated significant interest from the public and investors due to their decentralized nature, advanced security features, and potential for high returns. This research uses K-Means clustering and Inverse Covariance Clustering (ICC) to optimize cryptocurrency portfolios by addressing market dynamics and traditional portfolio management limitations. The study involved three phases: collecting daily price data from the top 100 cryptocurrencies from January 2018 to January 2024, performing calculations to identify cryptocurrencies through clustering methods, and constructing and dynamically optimizing investment portfolios from early 2022 to early 2024. We evaluate the constructed portfolios against the Cryptocurrency Benchmark Index (CRIX) using metrics like the Sharpe and Treynor ratios. Results show that both clustering methods can create efficient portfolios, but their effectiveness varies with dataset characteristics and investor objectives. K-Means produces more diversified portfolios, while ICC yields lower volatility portfolios, with ICC generally outperforming K-Means compared to the CRIX index. The findings highlight the potential of clustering methods in enhancing cryptocurrency portfolio selection and suggest the need for further research on real-world applications and advanced techniques tailored for the cryptocurrency market.
